the impacts of manipulating task complexity on efl

GEMA Online™ Journal of Language Studies 1057 Volume 12(4), November 2012

ISSN: 1675-8021

The Impacts Of Manipulating Task Complexity On EFL Learners’

Performance

Masoud Saeedi [email protected]

English Department

Payam-e-Noor University, Najafabad, Iran

Saeed Ketabi

[email protected]

English Department Faculty of Foreign Languages

University of Isfahan, Iran

Shirin Rahimi Kazerooni

[email protected]

English Department

Islamic Azad University of Khorasgan, Iran

Abstract

The purpose of this study is to investigate the impact of manipulating the cognitive

complexity of tasks on EFL learners’ narrative task performance in terms of complexity,

accuracy, and fluency of their production. To this aim, by drawing upon Robinson’s

(2007) Triadic Componential Framework (TCF), four levels of task complexity were

operationalized. Sixty- five Iranian students studying English as a foreign language at the

intermediate level participated in this research. The obtained results revealed that

manipulating different dimensions of task complexity exerts differential effects on

complexity, accuracy, and fluency of learners’ narrative task performance. Additionally,

it was shown that keeping tasks simple along the resource-dispersing dimension, while

making them more demanding along the resource-directing dimension results in a

simultaneous increase in complexity and accuracy, a finding which conforms to

predictions based on Robinson’s Cognition Hypothesis. These findings suggest that task

complexity can be used as a robust basis for making grading and sequencing decisions in

task-based syllabi.

Keywords: task complexity; structural complexity; lexical complexity; accuracy; fluency

Introduction

Defining and determining task complexity (TC) is of central importance in task-based

language teaching because with such knowledge educators can have a better

understanding of task performance, design, and development. TC can also inform grading

and sequencing decisions in a language teaching syllabus (Ellis, 2003; Skehan, 1998;

Robinson, 2001). The centrality of TC has inspired a growing number of studies

investigating a set of task characteristics, task types, and performance conditions which

are assumed to affect the difficulty of tasks well as learner performance. When describing

mailto:[email protected]




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tasks, previous research mainly used variables from a cognitive, information-processing

perspective to operationalize difficulty of tasks. Overall, previous findings have

confirmed that manipulating the psycholinguistic dimensions of TC has consistent effects

on features of L2 oral output, such as accuracy, fluency, and complexity. For instance,

tasks that use unfamiliar information, involve numerous steps for completion, and

provide no planning time are considered more difficult to perform than simpler, familiar

tasks that involve only a few operations and provide plenty of planning time. The study

reported in this article examined the synergistic effects of manipulating cognitive

complexity of narrative tasks along different dimensions on complexity, accuracy, and

fluency of Iranian EFL learners’ oral production. To this aim, by manipulating cognitive

demands of narrative tasks along planning time, single task demand, and the degree of

displaced, past time reference, four levels of TC were operationally defined.

To date, a number of studies have researched into the effects of these task factors in

isolation (see Robinson, 2011 for an updated and informative review). However, the

simultaneous effects of these three variables on quality of production have not been

investigated so far. This study was aimed at covering this lacuna.

Theoretical Background

Within the cognitive information-processing perspective to task-based research, a

considerable bulk of research has been motivated by different yet complementary models

for conceptualizing TC. These frameworks are sketched below. It is essential to mention

that there are other valuable emerging approaches (e.g., Van den Branden, 2006; Eckerth,

2008) which for reasons of space will not be discussed here.

Skehan’s Cognitive Approach

Drawing on Candlin’s (1987) framework for task difficulty (a term which in Skehan’s

framework is interchangeable with task complexity), and inspired by a cognitive

information-processing perspective to language learning, Skehan (1996, 1998) proposed

a three-way distinction for the analysis of task difficulty to which learner factors can also

be added: code complexity (vocabulary load and variety; linguistic complexity and

variety); cognitive complexity (familiarity of topic, discourse or task; amount of

computation and organization, and sufficiency of information); communicative stress

(time pressure; scale; number of participants; length of text; modality; stakes; opportunity

for control); and learner factors (intelligence; breadth of imagination; personal

experience).Skehan took linguistic complexity to be a “surrogate” of learners’

willingness to stretch their inter-language by experimenting with more difficult forms and

by trying out more elaborate language. He further argued that task difficulty is the

amount of attention the task demands from the participants. His predictions are premised

on a limited-capacity conception of attention which suggests that when task demands are

high, attention can only be allocated to certain aspects of performance to the detriment of

others. This tension is portrayed in his Tarde-off Hypothesis which predicts that there is a

tension between form (complexity and accuracy), on the one hand, and fluency, on the

other.


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Robinson’s Cognition Hypothesis

Robinson (2001, p.28) claimed that, “task complexity is the result of the attentional,

memory, reasoning, and other information-processing demands imposed by the structure

of the task on the language learner. These differences in information-processing

demands, resulting from design characteristics, are relatively fixed and invariant.”

Regarding attentional resources, Robinson has proposed that the human brain has a

multiple-resource attentional system, i.e., depletion of attention in one pool has no effect

on the amount remaining in another. In this view, attention, as suggested by models such

as Wickens’ (1992), can draw on multiple resources. To guide research into these claims,

and also pedagogy, Robinson (2007) proposed an operational taxonomy of task

characteristics. This taxonomic, Triadic Componential Framework (TCF) distinguishes

three categories of task. Task condition refers to interactive demands of tasks, including

participation variables (e.g., open vs. closed tasks) and participant variables (e.g., same

vs. different gender). A second category of task difficulty has to do with individual

differences in learner factors, such as working memory capacity, which can impact the

extent to which learners perceive task demands to be difficult to meet. These factors,

Robinson argued, explain why two learners may find the same task to be more or less

difficult than each other. The last component, task complexity, refers to the cognitive

demands of tasks, such as their reasoning demands (Robinson, 2011).

The TCF divides task features affecting the cognitive complexity of tasks along two

dimensions. Resource-directing dimensions of complexity affect allocation of cognitive

resources to specific aspects of L2 code. As stated by Robinson (2011, p.15), “By

increasing complexity along these dimensions, initially implicit knowledge of the L1

concept-structuring function of language becomes gradually explicit and available for

change during L2 production.” In contrast, resource-dispersing dimensions do not do

this: Increasing complexity along these dimensions reduces attentional and memory

resources with negative consequences for production, a position which is in agreement

with Skehan’s (1998). According to Robinson (2011), despite such negative

consequences, progressively increasing complexity along resource-dispersing variables is

also important in order to approximate the complexity conditions under which real-world

tasks are performed. Increasing task demands along these dimensions gradually removes

processing support for access to current inter-language; consequently, practice along

them requires faster and more automatic L2 access and use. One of the main claims of

Robinson’s Cognitive Hypothesis is that increasing task complexity along resource-

directing dimensions will be associated with simultaneous increases in complexity and

accuracy, a claim which contrasts with Skehan’s Trade-off Hypothesis prediction.

The Present Study

As was mentioned above, Robinson (2007) assumes that increasing task complexity

along resource-directing dimensions of cognitive complexity (e.g., +/- Here-and-Now)

will be associated with simultaneous increases in complexity and accuracy, a position

which contrasts with Skehan’s (1998). On the other hand, Robinson argues, increasing

complexity along resource-dispersing dimensions (e.g., +/- planning time, +/- single task)


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reduces attentional and memory resources with negative consequences for production, a

position which is in agreement with Skehan’s. According to Robinson, overall, the

predictions regarding the effects of task complexity on task performance have received

some support from previous studies mentioned above. However, as asserted by Robinson

(2001), “Synergetic effects of these resource-directing and resource-dispersing

dimensions can be expected and research is needed to investigate these” (p. 35). Few

studies (e.g., Gilabert, 2007), however, have simultaneously manipulated these task

complexity dimensions to look into potential synergetic effects they exert on task

performance. In point of fact, there seems to be a gap in the existing literature regarding

studies exploring the potential synergetic effects of simultaneously manipulating task

complexity along different dimensions with the aim of investigating its effect(s) on EFL

learners’ task performance. In response to the need for further research investigating task

complexity, the current research study was developed.

Drawing on Robinson’s (2007) TCF, the researchers intend to explore whether and how

manipulating task complexity along resource-directing and resource-dispersing

dimensions of task complexity synergistically impacts learners’ task performance. They

are specifically interested in investigating three variables which previous research have

suggested may affect narrative task performance: planning time, single task, and

Here/Now variables. In doing so, the researchers manipulated the complexity of narrative

tasks along two resource-dispersing variables of planning and single task together with

the resource-directing variable of Here/Now. Accordingly, the present study aims at

investigating the following research questions:

1) How does increasing the cognitive complexity of tasks simultaneously along

planning time, single task, and the Here/Now variables affect the complexity,

accuracy, and fluency of learners’ production?

2) Does making tasks more cognitively demanding along the resource-directing

dimension while keeping them simple along the resource-dispersing dimension

bring about a simultaneous increase in complexity and accuracy?

Methodology

Participants

Sixty-five Iranian L2 learners of English at a language institute in Isfahan, Iran

participated in this study on a volunteer basis. Participants were adult male Persian

speakers aged between 14 and 38 and attended the classes twice a week during a three-

month term. They were assigned to intermediate-level classes based on a placement test

and a short oral interview. Saeedi et al., (2010), investigated the criterion-related validity

of this placement test. They reported significant correlations among participants’ scores

on the placement test and the criteria.

Design

This study was a between-groups design. As such, each participant performed only one of

the four tasks of different degrees of cognitive demand operationalized below.


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Participants were randomly assigned to tasks. In order to investigate the statistical

significance of mean differences, a one-way MANOVA was carried out. In the analysis

process, an independent variable (i.e., task complexity) with four levels and four related

dependent variables were analyzed. These included: fluency, lexical complexity,

structural complexity, and accuracy of learners’ production.

Instruments

In order to have conformity with previous research in this area and, consequently,

enhance the comparability of results, narrative tasks were employed in this study.

Narrative tasks- retelling of stories based on sequenced sets of picture prompts or videos-

have been widely used in task-based research for a variety of objectives. Because such

tasks are non-interactive and fairly open to control, they have been popular among

researchers (Skehan & Foster, 1999). Four video episodes were chosen as ideal narrative

tasks because the episodes were (a) not too long; (b) easy to follow, without any cultural

bias; and (c) absorbing and engaging, so that telling the story would be something the

participants would be likely to enjoy. Following Skehan and Foster (1999), the data

collection design assumed that stories were similar to one another and that what made a

difference in performance was the condition under which each story was performed.

Building on Robinson’s (2007) TCF, four levels of task complexity were operationalized

(see Table 1). It was hypothesized that the first and the fourth task conditions would be

the least and the most cognitively demanding ones, respectively. The tasks were then pre-

piloted and piloted with Iranian EFL learners.

Table 1: Task complexity across dimensions

Complexity dimensions

Tasks

Simple Complex

Planning

Single task

Here/Now

A +

+

+

B -

-

+

C +

+

-

D -

-

-

Procedure

Data were collected over a period of some weeks. Under the first condition, following

Ellis (2009), participants were given 10 minutes as planning time before performing Task

A (see Appendix A). During this time participants were asked to do some activities

(+planning). The purpose of these activities was to highlight the relevant lexical items

and also familiarize participants with the topic. As for the operationalization of Here-and-

Now/There-and-Then distinction, after watching the video, each participant was asked to

perform the task of narrating the story in the present (see Robinson, 1995; Rahimpour,

1997; Gilabert, 2007).

Concerning Task B, participants who took this task were not given any planning time

(-planning). Furthermore, they were asked to do the secondary task of answering some

questions pertaining to the story content as they were watching the video (see Appendix


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B). Following watching the episode, they were also asked to do the main task of narrating

the story in the present (-single task; +Here/Now). As for the third task, each participant

who took Task C was given a ten-minute planning time to do a couple of activities (see

Appendix C). Having watched the video, each participant was asked to perform the single

task of retelling the story in the past tense (+ planning, +single task; -Here/Now).

Regarding the fourth task, participants were not given any planning time (-planning)

before retelling Task D. In addition, they were required to answer some comprehension

questions pertaining to the content of the episode while they were watching it (see

Appendix D). Following watching the video, they were also asked to carry out the main

task of narrating what they saw in the past (-single task; -Here/Now). Following

procedures developed in Foster and Skehan (1996), the audio-taped data were transcribed

and coded to measure participants’ performance in terms of structural complexity,

accuracy, fluency, and lexical complexity. These aspects of task performance were

operationalized as follows:

Structural Complexity

This aspect of performance was measured by counting the number of clauses and

dividing it by the total number of T-units. A T-unit is a main clause together with any

other clause(s) dependent on it. It should be noted that the T-unit was preferred to C-

unit, because this research dealt with one-way, monologic narratives which were

expected to trigger no elliptical answers (see Gilabert, 2007).

Accuracy

Accuracy of performance was measured by calculating the number of error-free clauses

as a percentage of the total number of clauses. This operationalization of accuracy was

motivated by findings of previous research indicating the sensitivity of such a global

measure of accuracy to detecting differences between experimental conditions (Skehan &

Foster, 1999). The criteria for defining error-free clauses were lack of errors with regard

to syntax, morphology, native-like lexical choice or word order. In general, the native-

like use of the language, in terms of grammar and lexis, was used as a criterion in

determining error-free clauses (see Tavakoli, 2009)

Fluency

Among the wide variety of approaches to measuring fluency, in this study, the rate of

pruned speech was chosen to code and measure each narrative because it includes both

the amount of speech and the length of pauses (Gilabert, 2007). Contrary to un-pruned

speech rate, in pruned speech rate, repetitions, reformulations, false starts, and asides in

the L1 are not considered in the calculation (Lennon, 1991). Pruned speech rate was

calculated by dividing the number of syllables by the total number of seconds and

multiplied by 60.

Lexical complexity

Recent research has shown that complexity, accuracy, and fluency need to be

supplemented by measures of lexical use (Skehan, 2009).This area, however, has been

strikingly absent in task research. This, according to Skehan (2009, p. 514) is a “serious

omission”. In this study, the Guiraud’s index of lexical richness, a variation of type/token


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ratio (TTR) was used to analyze lexical use. The advantage of this measure is that by

including the square root of the tokens it compensates for differences in text length. The

Guiraud’s Index of lexical richness was calculated by dividing the number of types by the

square root of the number of tokens.

Results

The results of participants’ task performance are displayed in Table 2. The table shows

the descriptive statistics for all measures. In order to investigate the statistical

significance of mean differences a multivariate analysis of variance (MANOVA) was

also run. Where significance was reached, a Post hoc Scheffe test was also carried out to

explore where the significant differences were located.

Table 2: Descriptive statistics for task performance: means and standard deviations

Tasks Lexical

complexity

Structural

complexity

Accuracy Fluency N

Mean SD Mean SD Mean SD Mean SD

Task A 5.57 .50 1.58 .13 40% 7% 113.75 18.77 16

Task B 5.10 .42 1.46 .19 32% 10% 94.31 19.92 16

Task C 5.45 .33 1.78 .12 56% 8% 108.62 14.92 16

Task D 4.92 .41 1.62 .16 45% 6% 90.94 15.86 17

The impact of manipulating task complexity along the resource-dispersing

dimension: Planning and single task variables

As shown in Table 3, there was a significant main effect for lexical complexity,

F (61, 3) = 8.235, p < .01, suggesting that lexical complexity was affected by the different

degrees of complexity. The results of Post hoc Scheffe test reported in Table 4 revealed

that the planned, +Here/Now, +single task triggered significantly more fluent speech (p <

.05) than the unplanned, +Here/Now, -single task one. Similarly, the +single task, -

Here/Now task performed under planned condition generated significantly more fluent

speech (p < .05) than the -single task, -Here/Now task performed under unplanned

condition (see Figure1).

Regarding structural complexity, there was a significant main effect, F (61, 3) =11.432, p

< .01. Results of Post hoc Scheffe test showed that structural complexity mean difference

between the planned, ,+single task, +Here/Now and the unplanned, -single task,

+Here/Now tasks did not reach statistical significance (p > .05). However, there was a

statistically significant mean difference between the planned, +single task , -Here/Now

and the unplanned, -single task, -Here/Now tasks (p < .05). Therefore, though Task A

(i.e., +Here/Now, +single task) generated a slightly higher level of structural complexity

than Task B (i.e., +Here/Now, -single task), the mean difference was not statistically

significant. The mean difference of structural complexity between performance on Task

C and Task D, on the contrary, was statistically significant (see Table 4).


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Table 3: One-way MANOVA by condition: main effects obtained for all measures across

different task complexity conditions

Source Dependent variable Df Mean square F-value P-value

Task complexity

Lexical complexity 61,3 1.478 8.235 .000**

Structural complexity 61,3 .290 11.432 .000**

Accuracy 61,3 .154 22.019 .000**

Fluency 61,3 1977.142 6.482 .001**

** p < .01

With regard to the measure of accuracy, there was a statistically significant main effect,

F (61, 3) = 22.019, p < .01. More specifically, as shown in Table 4, the simple task

performed under the planned, +Here/Now, +single task condition generated a slightly

higher percentage of error-free clauses than the one performed under the more complex

unplanned, +Here/Now, -single task condition. The mean difference, however, failed to

reach a statistically significant level (p >.05). On the other hand, Task C performed under

planned, -Here/Now, +single task condition elicited a significantly more accurate

performance than Task D performed under unplanned, -Here/Now, and -single task

condition (p < .05).

As for the fluency measure, there was a significant main effect, F (61, 3) = 6.482, p < .01,

which suggests that fluency was affected by the different degrees of complexity. The

results of Post hoc Scheffe test showed that both simple, + single task, + Here/Now and

more complex, +single task and -Here/Now conditions elicited a significantly higher

speech rate when performed under planned condition. More specifically, the planned,

+Here/Now, and +single task triggered significantly more fluent speech (p < .05) than the

unplanned, +Here/Now, -single task. Similarly, the -Here/Now and +single task

performed under planned condition generated a significantly more fluent performance (p

< .05) than the -Here/Now and -single task performed under unplanned condition (see

Table 4 and Figure 2).

In sum, on the basis of the obtained results, it can be deduced that manipulating task

complexity along the “resource-dispersing” dimension of tasks (i.e., planning time and

single-task) had a significant effect on fluency and lexical complexity of narrative task

performance but not on structural complexity or accuracy.

Figure1: Lexical complexity measures under different task complexity conditions


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Figure 2: Fluency measures under different task complexity conditions

Table 4: Mean differences between participants’ performance on simple and complex

tasks: the effects of planning and single-task variables

Comparison Lexical

complexity

Structural

complexity

Accuracy Fluency

+Planning, +single task,+

Here/Now (Task A) vs. -

planning, - single task ,

+ Here/Now (Task B)

.46* .12 .08 19.43

*

+Planning, +single task, -Here/

Now (Task C) vs. -planning, -

single task,

- Here/Now (Task D)

.53* .16

* .23

* 17.68

*

*p < .05

Impact of manipulating task complexity along the resource-directing dimension:

The Here/Now variable

As displayed in Table 5, the lexical complexity of participants’ performance on the

simple planned, +Here/Now task (Task A) was a bit higher than that of their counterparts

who took the more cognitively demanding planned, -Here/Now task (Task C). The means

difference, however, was not statistically significant (p > .05). Similarly, though taking

the simple unplanned, +Here/Now task (Task B) caused learners to have a more lexically

complex task performance than those who took the more complex unplanned, -Here/Now

task (Task D), the mean difference between their lexical complexity measure failed to

reach statistical significance (p > .05). As for structural complexity, participants who took

the more complex -Here/ Now under both planned and unplanned conditions had a more

structurally complex performance than those who took the simpler +Here/Now condition

(see Figure 3). The result of Post hoc Scheffe test showed these differences to be

statistically significant (p < .05).

The reported results pertaining to the accuracy measure displayed that increasing

complexity along the +/- Here/Now variable positively affected learners’ accuracy of

performance. The percentage of error-free clauses showed more attention paid to

accuracy of speech when tasks were performed in the complex -Here/Now than when

produced under the simpler +Here/Now condition. In other words, complex planned tasks


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under the -Here/Now condition triggered a significantly (p<.05) higher proportion of

error-free clauses than the simpler planned, +Here/Now tasks. This was also shown to be

the case when tasks were performed under the unplanned condition (see Figure 4).

Finally, regarding fluency of speech, though learners’ performance under the simple

+ Here/Now condition was more fluent than that of their counterparts who took the

complex -Here/Now condition, the Post hoc Sheffe test, however, did not confirm the

statistical significance of the mean difference (p >.05). This was the same between simple

and complex tasks when performed under both planned and unplanned conditions.

To summarize, the results of data analyses reported in this section revealed, manipulating

task complexity along the “resource-directing” dimension of tasks (i.e., the Here/Now

variable) had a significant effect on structural complexity and accuracy, but not lexical

complexity or fluency of learners’ production.

Table 5: Mean differences between participants’ performance on simple and complex

tasks: the effect of Here/Now variable

Comparison

Lexical

complexity

Structural

complexity

Accuracy Fluency

+Planning, +single task,+

Here/Now (Task A) vs.

+Planning, +single task,

-Here/ Now (Task C)

.11 -.20* -.15

* 5.12

-Planning, - single task ,+ Here-

Now (Task B) vs. -planning, -

single task,

- Here/Now (Task D)

.17 -.16* -.12

* 3.37

*p < .05

Figure 3: Structural complexity measures under different task complexity conditions


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Figure 4: Accuracy measures under different task complexity conditions

Effects of simultaneous manipulation of task complexity along resource-dispersing

and resource-directing dimensions: +/-planning time, +/- single task, and +/-

Here/Now

A comparison between task performances under different conditions revealed that

reducing task complexity along resource-dispersing dimensions (i.e., +/-planning and +/-

single task) and increasing it along the resource-directing one (i.e., +/- Here/Now) has

simultaneously raised structural complexity and accuracy of production. This significant

finding seems to bear out Robinson’s (2007) claims regarding the synergistic effects of

manipulating task complexity along resource-dispersing and resource-directing

dimensions. The results of Post hoc Scheffe test comparing performance on Task C to

performances on the three other conditions are reported in Table 6 below. As reported in

the table, participants who took Task C had the optimum performance in terms of

accuracy and fluency of their production.

Table 6: Mean differences: Task C compared to other tasks

Comparison Lexical complexity Structural complexity Accuracy Fluency

Task C vs. Task A

Task C vs. Task B

Task C vs. Task D

-.11

.35

.53*

.20*

.32*

.16*

.15*

.23*

.10*

-5.12

14.31

17.68*

*P < .05

Discussion

This study was primarily aimed at examining the effects of simultaneously manipulating

the resource-dispersing and resource-directing dimension of task complexity on learners’

accuracy, complexity, and fluency narrative task performance. At this section, the

findings of the study will be summarized and discussed in turn.

Regarding the first research question, it was shown that manipulating pre-task planning

time had a significant effect on fluency and lexical complexity of oral task performance

but not on structural complexity or accuracy. It was also found out that manipulating

cognitive complexity of tasks along “single-task” dimension had a significant effect on


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fluency and lexical complexity but not on structural complexity or accuracy of

participants’ oral task performance. Additionally, it was shown that increasing the

cognitive complexity of tasks along the degree of displaced, past time reference (i.e., -

Here/Now) enhances structural complexity and accuracy. Manipulating this variable,

however, did not affect fluency and lexical complexity of participants’ oral task

performance.

The effect of simplifying Task A along both resource-directing and resource-dispersing

dimensions was a fluent as well as lexically complex production. However, this condition

did not channel learners’ attention to the way the conveyed their message. This shows

that fluency is enhanced when processing demands are low. If processing load is reduced,

by the effect of providing pre-task planning time, fluency increases. This further revealed

that fluency is clearly sensitive to processing. Drawing on the Levelt (1989) model of

speaking, it can be argued that pre-task planning does not significantly assist the

formulation stage of production. Rather, it focuses attention on conceptualizing the

message which results in increased fluency and complexity rather than accuracy of

production. In addition, higher fluency is not the consequence of attention allocation, as

complexity and accuracy would be, but the consequence of more efficient message

planning and faster lexical access and selection.

The second condition was made complex along resource-dispersing dimension by not

allotting any pre-task planning time and adding a secondary task. It was also kept simple

along resource-directing dimension by present time reference. This resulted in disfluency.

Moreover, it negatively affected lexical and structural complexity as well as accuracy of

participants’ performance on Task B. With regard to the negative effect of adding a

secondary task to the primary task on fluency, Wickens (1989, p.73) has suggested that

when a secondary task is added to a primary task, confusion between the tasks may lead

to poor performance. This strategy will extract a toll on resources. These findings also

bear out Robinson’s (2001, 2007) speculations as to the effects of increasing cognitive

demands of tasks by manipulating resource-dispersing variables. He claims that

increasing complexity along resource-dispersing dimensions (e.g., +/- planning time, +/-

single task) reduces attentional and memory resources with negative consequences for

production, a position which is in agreement with Skehan’s (1998).

Task C was was made cognitively demanding along resource-directing dimension

through displaced, past time reference, but kept simple along resource-dispersing

dimension by providing pre-task planning time. Post hoc means comparisons showed that

participants who performed Task C outperformed others in terms of accuracy and

structural complexity of their performance. This finding addresses the second research

question posed pertaining to the synergistic effect of increasing task complexity along the

resource-directing dimension and reducing it along the resource-dispersing dimension.

The results suggest that completing the task under this condition can make for a

simultaneous increase in accuracy and complexity, which provides further evidence in

support of Robinson’s predictions. He maintains that if tasks are kept simple along

resource-dispersing dimensions (e.g., +/- planning time, +/- single task), but are made

more cognitively demanding along resource-directing variables (e.g., +/- Here/Now),


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attention may be simultaneously channeled toward accuracy and complexity, a positions

which contrasts with Skehan’s. Drawing upon Levelt’s model (1989) of speech

production, Robinson has tried to provide a psycholinguistic rationale for the way task

demands affect speech production. He argued that increasing the conceptual demands of

tasks results in greater effort at conceptualization and “macroplanning” at the message

preparation stage, thus “creating the conditions for development and re-mapping of

conceptual and linguistic categories” (Robinson, Cadierno, & Shirai, 2009, p. 537, as

cited in Robinson, 2011), during subsequent “microplanning” and the lexicogrammatical

encoding stage into which macroplanning feeds. According to Robinson (2011, p. 16), in

Levelt’s model, the conceptualization stage generates a “preverbal message”: “the

message should contain the features that are necessary and sufficient for the next stage of

processing- specially for grammatical encoding” (Levelt, 1989, p. 70). Therefore, greater

effort at conceptualization during message preparation, caused by conceptually

demanding tasks, should lead to “paring down” of conceptual information into a

“linguistically relevant representation” for subsequent encoding, at the microplanning

stage, with positive consequences for accurate and complex performance (Dipper, Black,

& Bryan, 2005, p. 422, as cited in Robinson, 2011).The fact that increasing task

complexity along the resource-directing dimension resulted in simultaneous increase in

accuracy and complexity also cannot be explained within the limited-capacity view,

according to which there are not enough attentional resources to focus on complexity and

accuracy simultaneously. As was mentioned above, drawing on the limited-capacity view

of attention, Foster and Skehan (1996), Skehan and Foster (1997), and also Mehnert

(1998) have hypothesized that trade-off effects exist between accuracy and complexity.

They have hypothesized that any gains in complexity are achieved at the expense of

accuracy and vice versa.

Finally, Task D was made complex at both resource-directing and resource-dispersing

levels through displaced, past time reference and not providing pre-task planning. A

comparison between performance on Task C and Task D shows that the latter elicited a

significantly poorer performance in all dimensions of production. On the contrary,

compared with Task A and Task B, performance on this task was better in terms of

accuracy and structural complexity but not fluency and lexical complexity.

Conclusion

This research added to the existing literature by simultaneously manipulating cognitive

demands of narrative tasks along planning time, single task demand, and degree of

displaced, past time reference since these variables have often been researched in

isolation. The major contribution that this study makes is the discovery that

simultaneously manipulating task demands along the above-mentioned variables can

differentially affect complexity, accuracy, and fluency of EFL learners’ oral production.

Thus, the experimental operationalization and manipulation of different aspects of task

design can be transferred to pedagogic contexts in order to attain specific effects on

production and, possibly, learning. On the basis of the outcomes, it might be argued that

L2 task designers need to observe the cognitive demands of a task as a key consideration

in their choice, design, and sequencing of L2 teaching tasks. In this respect, Samuda


ISSN: 1675-8021

(2001) maintains that defining task complexity is essential to a rigorous evaluation of

task design required for both classroom practices and teacher development programs.

Additionally, the findings reported here have significant implications for language

testing. To be able to design assessment which is fair, valid, and reliable, it is crucial for

language testing to use tasks of appropriate level of cognitive demands.

Many other studies can be carried out in this area of research to enhance confidence in

making pedagogic decisions regarding the implications of task complexity for grading

and sequencing decisions and its impact on task performance. Future research can bring

into the picture the impact of other difficulty factors on task-based performance not

considered in this study, such as aptitude, intelligence, motivation, and proficiency level

(see Chalak & Kassaian, 2010). To alleviate the shortcomings of this research, future

studies may also adopt different measures and methodologies. Additionally, as this study

mainly focused on cognitive complexity of tasks, the possible effects of some other

potentially important variables within the variables manipulated (e.g. different types,

sources, and foci of planning ) as well as the tasks used were not examined. Further

research is needed to probe into the potential effects of such variables on learners’ task

performance.

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APPENDIX A

Pre-task Planning Activities for Task A

a) What things can you use to cook chicken? Put the words in the chart. Can you add

four more words?

bread crumbs butter flour oil

a frying pan a stove salt a knife

an oven a refrigerator a saucepan

Kitchen appliances Cooking utensils Cooking ingredients

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b) What’s your favorite...?

main dish ------------------------------------ dessert -----------------------------------

Vegetable ------------------------------------ snack -----------------------------------

c) What food do you like to cook?

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APPENDIX B

Secondary Task for Task B

Who said the following sentences? Check the correct answers as you watch the video.

1) Will you see if he can come in…tomorrow

morning…oh, around 10:15?------------------------------------

2) I'd really like to have someone by Saturday.---------------

3) I think I'm good with people.---------------------------------

4) I'm very patient-------------------------------------------------

5) I worked on weekends while I was in school.--------------

6) we need someone who is good with money.---------------

7) We're really looking for someone who can make people

laugh.

8) How did you know I could use these? ----------------------

Martha Bob David Greg


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APPENDIX C

Pre-task Planning Activities for Task C

a) What do you think are the most important factors in renting an apartment? Number

the items below from 1 (most important) to 8 (least important).

b) In what neighborhood do you live? What's it like?

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c) Do you live in a house or an apartment? Which one do you prefer? Why?

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APPENDIX D

Secondary Task for Task D

Who said the sentences bellow? Check the correct answers as you watch the video.

1) I wonder if people are upset.-------------------------------------

2) It probably means they're worried that things might change.

3) Can you have lunch with me today? ----------------------------

4) Maybe we can meet later this afternoon.-----------------------

5) Could you come into my office please? ------------------------

6) I think she went to see the dentist.-------------------------------

7) That's strange.------------------------------------------------------

8) The office staff isn't allowed to hold birthday parties.--------

10) You weren't supposed to come in yet.-------------------------

Julia Barbara Laurie

appliances

rent

view

location

security

other------------

noise

size


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About the authors

Masoud Saeedi is an assistant professor in TEFL at Payam-e-Noor University, Najafabad,

Iran. He has been involved in teaching English courses in different institutes and

universities in Iran. He is especially interested in Task-based language teaching and

Language assessment.

Saeed Ketabi is an assistant professor in TEFL at the University of Isfahan, Iran. He has

been teaching TEFL courses at the University of Isfahan for several years. His main

research interests are Second language syllabus design and Materials development.

Shirin Rahimi Kazerooni is a language instructor at Islamic Azad University of

Khorasgan, Isfahan, Iran. She holds an MA in TEFL. Her main areas of interest are

Teacher education and Task-based language learning and teaching.

the impacts of manipulating task complexity on efl

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